Awesome Deep Learning for Medical Imaging Segmentation

Contents

• Related Surveys
• Generative Models
• Few-Shot Learning
• Foundation Models
• Universal Models

🔍Related Surveys

Medical Image Segmentation Review: The Success of U-Net
Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland, Yiwei Jia, Atlas Haddadi Avval, and Afshin Bozorgpour
[TPAMI, 2024] [Paper] [ArXiv]

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications
Nahian Siddique, Sidike Paheding, Colin P. Elkin, and Vijay Devabhaktuni
[IEEE Access, 2021] [Paper] [ArXiv]

Advances in medical image analysis with vision Transformers: A comprehensive review
Reza Azad, Amirhossein Kazerouni, Moein Heidari, Ehsan Khodapanah Aghdam, Amirali Molaei, Yiwei Jia, Abin Jose, Rijo Roy, and Dorit Merhof
[MIA, 2024] [Paper] [ArXiv]

Transformers in Medical Imaging: A Survey
Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, and Huazhu Fu
[MIA, 2023] [Paper] [ArXiv]

Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Jun Li, Junyu Chen, Yucheng Tang, Ce Wang, Bennett A. Landman, and S. Kevin Zhou
[MIA, 2023] [Paper] [ArXiv]

Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions
Yichi Zhang, Zhenrong Shen, and Rushi Jiao
[CIBM, 2024] [Paper] [ArXiv]

🔍 Generative Models

Generative AI enables medical image segmentation in ultra low-data regimes
Li Zhang, Basu Jindal, Ahmed Alaa, Robert Weinreb, David Wilson, Eran Segal, James Zou, and Pengtao Xie
[Nat. Comm., 2025][Paper][Supp.]

Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation
Kunpeng Qiu, Zhiqiang Gao, Zhiying Zhou, Mingjie Sun, Yongxin Guo
[CVPR, 2025][Paper] [ArXiv] [Github]

FairDiff: Fair Segmentation with Point-Image Diffusion
Wenyi Li, Haoran Xu, Guiyu Zhang, Huan-ang Gao, Mingju Gao, Mengyu Wang, and Hao Zhao
[MICCAI, 2024] [Paper] [ArXiv] [Github]

DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model
Zheyuan Zhang, Lanhong Yao, Bin Wang, Debesh Jha, Gorkem Durak, Elif Keles, Alpay Medetalibeyoglu, and Ulas Bagci
[TMI, 2024] [Paper] [ArXiv] [Github]

C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation
Boah Kim, Yujin Oh, Bradford J. Wood, Ronald M. Summers, and Jong Chul Ye
[MIA, 2024] [Paper] [ArXiv] [Github]

MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer
Junde Wu, Wei Ji, Huazhu Fu, Min Xu, Yueming Jin, and Yanwu Xu
[AAAI, 2024] [Paper] [ArXiv] [Github]

MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
Junde Wu, Rao Fu, Huihui Fang, Yu Zhang, Yehui Yang, Haoyi Xiong, Huiying Liu, and Yanwu Xu
[MLR, 2024] [Paper] [ArXiv] [Github]

Diffusion adversarial representation learning for self-supervised vessel segmentation
Boah Kim, Yujin Oh, and Jong Chul Ye
[ICLR, 2023] [Paper] [ArXiv] [Github]

Self-Supervised Vessel Segmentation via Adversarial Learning
Yuxin Ma, Yang Hua, Hanming Deng, Tao Song, Hao Wang, Zhengui Xue, Heng Cao, Ruhui Ma, Haibing Guan
[ICCV, 2021] [Paper] [ArXiv] [Github]

Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks
Veit Sandfort , Ke Yan, perry J. pickhardt, and Ronald M. Summers
[SR, 2019] [Paper] [Github]

Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images
Faisal Mahmood, Daniel Borders, Richard Chen et al.
[TMI, 2019] [Paper] [ArXiv] [Github]

SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth
Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon et al.
[TMI, 2018] [Paper] [ArXiv] [Github]

Spine-GAN: Semantic segmentation of multiple spinal structures
Zhongyi Hana, Benzheng Wei, Ashley Mercado, Stephanie Leung, and Shuo Li
[MIA, 2018] [Paper] [ArXiv] [Github]

Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network
Zizhao Zhang, Lin Yang, and Yefeng Zheng
[CVPR, 1018] [Paper]

Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images
Yizhe Zhang, Lin Yang, Jianxu Chen, Maridel Fredericksen,David P. Hughes, and Danny Z. Chen
[MICCAI, 2017] [Paper] [ArXiv]

🔍 Few-Shot Learning

Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentation
Yumin Zhang, Hongliu Li, Yajun Gao, Haoran Duan, Yawen Huang, and Yefeng Zheng
[TMI, 2024] [Paper] [ArXiv]

Rethinking Few-Shot Medical Segmentation: A Vector Quantization View
Shiqi Huang, Tingfa Xu, Ning Shen, Feng Mu, and Jianan Li
[CVPR, 2023] [Paper]

Learning what and where to segment: A new perspective on medical image few-shot segmentation
Yong Feng, Yonghuai Wang, Honghe Li, Mingjun Qu, Jinzhu Yang
[MIA, 2023] [Paper] [Github]

Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration
Yiwen Li, Yunguan Fu, Iani J.M.B. Gayo et al.
[MIA, 2023] [Paper] [Github]

Few Shot Medical Image Segmentation with Cross Attention Transformer
Yi Lin, Yufan Chen, Kwang-Ting Cheng, and Hao Chen
[MICCAI, 2023] [Paper] [ArXiv] [Github]

Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels
Stine Hansen, Srishti Gautam, Robert Jenssen, Michael Kampffmeyer
[MIA, 2022] [Paper] [Github]

Dual Contrastive Learning with Anatomical Auxiliary Supervision for Few-shot Medical Image Segmentation
Huisi Wu, Fangyan Xiao, and Chongxin Liang
[ECCV, 2022] [Paper] [Github]

Recurrent Mask Refinement for Few-Shot Medical Image Segmentation
Hao Tang, Xingwei Liu, Shanlin Sun, Xiangyi Yan, and Xiaohui Xie
[ICCV, 2021] [Paper] [Github]

‘Squeeze & Excite’ Guided Few-Shot Segmentation of Volumetric Images
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl, Nassir Navab, and Christian Wachinger
[MIA, 2020] [Paper] [ArXiv] [Github]

Self-supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
Cheng Ouyang, Carlo Biffi, Chen Chen, Turkay Kart, Huaqi Qiu, and Daniel Rueckert
[ECCV, 2020] [Paper] [ArXiv] [Github]

🔍 Foundation Models

MedLSAM: Localize and segment anything model for 3D CT images
Wenhui Lei, Xu Wei, Xiaofan Zhang, Kang Li, and Shaoting Zhang
[MIA, 2025] [Paper][ArXiv] [Github]

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
Junde Wu, Wei Ji, Yuanpei Liu, Huazhu Fu, Min Xu, Yanwu Xu, and Yueming Jin
[MIA, 2025] [Paper][ArXiv] [Github]

Segment anything in medical images
Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang
[Nature C, 2024] [Paper] [ArXiv] [Github]

Segment Anything Model for Medical Images?
Yuhao Huang, Xin Yang, Lian Liu et al.
[MIA, 2024] [Paper] [ArXiv] [Github]

SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization
Yichi Zhang, Jin Yang, Yuchen Liu, Yuan Cheng, and Yuan Qi
[BIBM, 2024] [Paper] [ArXiv] [Github]

SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation
Wenxi Yue, Jing Zhang, Kun Hu, Yong Xia, Jiebo Luo, and Zhiyong Wang
[AAAI, 2024] [Paper] [ArXiv] [Github]

MA-SAM: Modality-agnostic SAM adaptation for 3D medical image segmentation
Cheng Chena, Juzheng Miaob, Dufan Wua et al.
[MIA, 2024] [Paper] [ArXiv] [Github]

FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images
Yiqing Shen, Jingxing Li, Xinyuan Shao, Blanca Inigo Romillo, Ankush Jindal, David Dreizin, and Mathias Unberath
[MICCAI, 2024] [Paper] [ArXiv] [Github]

S-SAM: SVD-Based Fine-Tuning of Segment Anything Model for Medical Image Segmentation
Jay N. Paranjape, Shameema Sikder, S. Swaroop Vedula, and Vishal M. Patel
[MICCAI, 2024] [Paper] [ArXiv] [Github]

SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images
Haoyu Wang, Sizheng Guo, Jin Ye et al.
[arXiv, 2024] [ArXiv] [Github]

AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation
*Jay N. Paranjape, Nithin Gopalakrishnan Nair, Shameema Sikder, S. Swaroop Vedula, and Vishal M. Patel *
[ACMIUA, 2024] [Paper] [ArXiv] [Github]

SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model
Tassilo Wald, Saikat Roy, Gregor Koehler, Nico Disch, Maximilian Rouven Rokuss, Julius Holzschuh, David Zimmerer, and Klaus Maier-Hein
[MIDL, 2023] [Paper] [ArXiv]

Segment anything model for medical image analysis: An experimental study
Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu, Jichen Yang, Nicholas Konz, Yixin Zhang
[MIA, 2023] [Paper] [ArXiv] [Github]

Customized Segment Anything Model for Medical Image Segmentation
Kaidong Zhang, and Dong Liu
[arXiv, 2023] [ArXiv] [Github]

SAM-Med2D
Junlong Cheng, Jin Ye, Zhongying Deng et al.
[arXiv, 2023] [ArXiv] [Github]

SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology
*Jingwei Zhang, Ke Ma, Saarthak Kapse, Joel Saltz, Maria Vakalopoulou, Prateek Prasanna, and Dimitris Samaras *
[MICCAI, 2023] [Paper] [ArXiv] [Github]

Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model
*Yizhe Zhang, Tao Zhou, Shuo Wang, Peixian Liang, Yejia Zhang, and Danny Z. Chen *
[MICCAIW, 2023"] [Paper] [ArXiv] [Github]

3DSAM-adapter: Holistic Adaptation of SAM from 2D to 3D for Promptable Medical Image Segmentation
Shizhan Gong, Yuan Zhong, Wenao Ma, Jinpeng Li, Zhao Wang, Jingyang Zhang, Pheng-Ann Heng, and Qi Dou
[arXiv, 2024] [ArXiv] [Github]

Segment Anything Model for Semi-supervised Medical Image Segmentation via Selecting Reliable Pseudo-labels
Ning Li, Lianjin Xiong, Wei Qiu, Yudong Pan, Yiqian Luo, and Yangsong Zhang
[ICNIP, 2023] [Paper] [Github]

🔍 Universal Models

Show and Segment: Universal Medical Image Segmentation via In-Context Learning
Yunhe Gao, Di Liu, Zhuowei Li, Yunsheng Li, Dongdong Chen, Mu Zhou, Dimitris N. Metaxas
[CVPR, 2025] [Paper]

ICL-SAM: Synergizing in-context learning model and SAM in medical image segmentation
Jiesi Hu, Jiesi_Hu1, Yang Shang, Yanwu Yang, Xutao Guo, Hanyang Peng, Ting Ma
[MIDL, 2024] [Paper]

Efficient in-context medical segmentation with meta-driven visual prompt selection
Chenwei Wu, David Restrepo, Zitao Shuai, Zhongming Liu, Liyue Shen
[MICCAI, 2024] [Paper] [ArXiv]

Tyche: Stochastic In-Context Learning for Medical Image Segmentation
Marianne Rakic, Hallee E. Wong, Jose Javier Gonzalez Ortiz, Beth A. Cimini, John V. Guttag, Adrian V. Dalca
[CVPR, 2024] [Paper] [Supp] [ArXiv] [Github]

UniSeg: A prompt-driven universal segmentation model as well as a strong representation learner
Yiwen Ye, Yutong Xie, Jianpeng Zhang, Ziyang Chen, Yong Xia
[MICCAI, 2023] [Paper] [Github]

MultiTalent: A multi-dataset approach to medical image segmentation
Constantin Ulrich, Fabian Isensee, Tassilo Wald, Maximilian Zenk, Michael Baumgartner, Klaus H. Maier-Hein
[MICCAI, 2023] [Paper] [ArXiv] [Github]

UniverSeg: Universal Medical Image Segmentation
Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R. Sabuncu, John Guttag, and Adrian V. Dalca
[ICCV, 2023] [Paper] [Supp] [ArXiv] [Github]

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Jie Liu, Yixiao Zhang, Jie-Neng Chen et al.
[ICCV, 2023] [Paper] [Supp] [Github]

DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
Jianpeng Zhang, Yutong Xie, Yong Xia, Chunhua Shen
[CVPR, 2021] [Paper] [Github]

Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction
Xi Fang, Pingkun Yan
[TMI, 2020] [Paper] [Github]

Med3d: Transfer learning for 3d medical image analysis
Sihong Chen, Kai Ma, Yefeng Zheng
[arXiv, 2019] [ArXiv] [Github]

Citation: If you found this Repository useful, please cite:

@article{bougourzi2025recent,
  title={Recent Advances in Medical Imaging Segmentation: A Survey},
  author={Bougourzi, Fares and Hadid, Abdenour},
  journal={arXiv preprint arXiv:2505.09274},
  year={2025}
}